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ID 30033
FullText URL
Author
Katai, Osamu
Abstract

Estimation of Bayesian network algorithms, which adopt Bayesian networks as the probabilistic model were one of the most sophisticated algorithms in the estimation of distribution algorithms. However the estimation of Bayesian network is key topic of this algorithm, conventional EBNAs adopt greedy searches to search for better network structures. In this paper, we propose a new EBNA, which adopts genetic algorithm to search the structure of Bayesian network. In order to reduce the computational complexity of estimating better network structures, we elaborates the fitness function of the GA module, based upon the synchronicity of specific pattern in the selected individuals. Several computational simulations on multidimensional knapsack problems show us the effectiveness of the proposed method.

Keywords
belief networks
computational complexity
distributed algorithms
genetic algorithms
knapsack problems
probability
Note
Digital Object Identifier: 10.1109/ICNNSP.2003.1279302
Published with permission from the copyright holder. This is the institute's copy, as published in Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on, 14-17 Dec. 2003, Vol. 1, Pages 436-439.
Publisher URL:http://dx.doi.org/10.1109/ICNNSP.2003.1279302
Copyright © 2003 IEEE. All rights reserved.
Published Date
2003-12
Publication Title
Neural Networks and Signal Processing
Volume
volume1
Start Page
436
End Page
439
Content Type
Journal Article
language
English
Refereed
True
DOI
Submission Path
industrial_engineering/25